Online Algorithms for Geographical Load Balancing

It has recently been proposed that Internet energy costs, both monetary and environmental, can be reduced by exploiting temporal variations and shifting processing to data centers located in regions where energy currently has low cost. Lightly loaded data centers can then turn off surplus servers. This paper studies online algorithms for determining the number of servers to leave on in each data center, and then uses these algorithms to study the environmental potential of geographical load balancing (GLB). A commonly suggested algorithm for this setting is “receding horizon control” (RHC), which computes the provisioning for the current time by optimizing over a window of predicted future loads. We show that RHC performs well in a homogeneous setting, in which all servers can serve all jobs equally well; however, we also prove that differences in propagation delays, servers, and electricity prices can cause RHC perform badly, So, we introduce variants of RHC that are guaranteed to perform as well in the face of such heterogeneity. These algorithms are then used to study the feasibility of powering a continent-wide set of data centers mostly by renewable sources, and to understand what portfolio of renewable energy is most effective

Planning and Management of Cloud Computing Networks

Résumé L’évolution de l’internet a un effet important sur une grande partie de la population mondiale. On l’utilise pour communiquer, consulter de l’information, travailler et se divertir. Son utilité exceptionnelle a conduit à une explosion de la quantité d’applications et de ressources informatiques. Cependant, la croissance du réseau entraîne une importante consommation énergétique. Si la consommation énergétique des réseaux de télécommunications et des centres de données était celle d’un pays, il se classerait 5e pays du monde. Pis, le nombre de serveurs dans le monde devrait être multiplié par 10 entre 2013 et 2020. Ce contexte nous a motivé à étudier des techniques et des méthodes pour affecter les ressources d’une façon optimale par rapport aux coûts, à la qualité de service, à la consommation énergétique et `a l’impact écologique. Les résultats que nous avons obtenus minimisent les dépenses d’investissement (CAPEX) et les dépenses d’exploitation (OPEX), réduisent d’un facteur 6 le temps de réponse, diminuent la consommation énergétique de 30% et divisent les émissions de CO2 par un facteur 60. L’infonuagique permet l’accès dynamique aux ressources informatiques comme un service. Les programmes sont exécutés sur des serveurs connectés `a l’internet, et les usagers peuvent les utiliser depuis leurs ordinateurs et dispositifs mobiles. Le premier avantage de cette architecture est de réduire le temps de mise en place des applications et l’interopérabilité. En effet, un nouvel utilisateur n’a besoin que d’un navigateur web. Il n’est forcé ni d’installer de programmes sur son ordinateur, ni de posséder un système d’exploitation spécifique. Le deuxième avantage est la disponibilité des applications et de l’information de fa ̧con continue. Celles-ci peuvent être utilisées `a partir de n’importe quel endroit et de n’importe quel dis- positif connecté `a l’internet. De plus, les serveurs et les ressources informatiques peuvent être affectés aux applications de fa ̧con dynamique, selon la quantité d’utilisateurs et la charge de travail. C’est ce que l’on appelle l’élasticité des applications.---------- Abstract The evolution of the Internet has a great impact on a big part of the population. People use it to communicate, query information, receive news, work, and as entertainment. Its extraordinary usefulness as a communication media made the number of applications and technological resources explode. However, that network expansion comes at the cost of an important power consumption. If the power consumption of telecommunication networks and data centers is considered as the power consumption of a country, it would rank at the 5th place in the world. Furthermore, the number of servers in the world is expected to grow by a factor of 10 between 2013 and 2020. This context motivates us to study techniques and methods to allocate cloud computing resources in an optimal way with respect to cost, quality of service (QoS), power consumption, and environmental impact. The results we obtained from our test cases show that besides minimizing capital expenditures (CAPEX) and operational expenditures (OPEX), the response time can be reduced up to 6 times, power consumption by 30%, and CO2 emissions by a factor of 60. Cloud computing provides dynamic access to IT resources as a service. In this paradigm, programs are executed in servers connected to the Internet that users access from their computers and mobile devices. The first advantage of this architecture is to reduce the time of application deployment and interoperability, because a new user only needs a web browser and does not need to install software on local computers with specific operating systems. Second, applications and information are available from everywhere and with any device with an Internet access

QoS-aware service continuity in the virtualized edge

5G systems are envisioned to support numerous delay-sensitive applications such as the tactile Internet, mobile gaming, and augmented reality. Such applications impose new demands on service providers in terms of the quality of service (QoS) provided to the end-users. Achieving these demands in mobile 5G-enabled networks represent a technical and administrative challenge. One of the solutions proposed is to provide cloud computing capabilities at the edge of the network. In such vision, services are cloudified and encapsulated within the virtual machines or containers placed in cloud hosts at the network access layer. To enable ultrashort processing times and immediate service response, fast instantiation, and migration of service instances between edge nodes are mandatory to cope with the consequences of user’s mobility. This paper surveys the techniques proposed for service migration at the edge of the network. We focus on QoS-aware service instantiation and migration approaches, comparing the mechanisms followed and emphasizing their advantages and disadvantages. Then, we highlight the open research challenges still left unhandled.publishe

Efficient Resource Management for Cloud Computing Environments

Cloud computing has recently gained popularity as a cost-effective model for hosting and delivering services over the Internet. In a cloud computing environment, a cloud provider packages its physical resources in data centers into virtual resources and offers them to service providers using a pay-as-you-go pricing model. Meanwhile, a service provider uses the rented virtual resources to host its services. This large-scale multi-tenant architecture of cloud computing systems raises key challenges regarding how data centers resources should be controlled and managed by both service and cloud providers. This thesis addresses several key challenges pertaining to resource management in cloud environments. From the perspective of service providers, we address the problem of selecting appropriate data centers for service hosting with consideration of resource price, service quality as well as dynamic reconfiguration costs. From the perspective of cloud providers, as it has been reported that workload in real data centers can be typically divided into server-based applications and MapReduce applications with different performance and scheduling criteria, we provide separate resource management solutions for each type of workloads. For server-based applications, we provide a dynamic capacity provisioning scheme that dynamically adjusts the number of active servers to achieve the best trade-off between energy savings and scheduling delay, while considering heterogeneous resource characteristics of both workload and physical machines. For MapReduce applications, we first analyzed task run-time resource consumption of a large variety of MapReduce jobs and discovered it can vary significantly over-time, depending on the phase the task is currently executing. We then present a novel scheduling algorithm that controls task execution at the level of phases with the aim of improving both job running time and resource utilization. Through detailed simulations and experiments using real cloud clusters, we have found our proposed solutions achieve substantial gain compared to current state-of-art resource management solutions, and therefore have strong implications in the design of real cloud resource management systems in practice

Efficient Resource Management for Cloud Computing Environments

Cloud computing has recently gained popularity as a cost-effective model for hosting and delivering services over the Internet. In a cloud computing environment, a cloud provider packages its physical resources in data centers into virtual resources and offers them to service providers using a pay-as-you-go pricing model. Meanwhile, a service provider uses the rented virtual resources to host its services. This large-scale multi-tenant architecture of cloud computing systems raises key challenges regarding how data centers resources should be controlled and managed by both service and cloud providers. This thesis addresses several key challenges pertaining to resource management in cloud environments. From the perspective of service providers, we address the problem of selecting appropriate data centers for service hosting with consideration of resource price, service quality as well as dynamic reconfiguration costs. From the perspective of cloud providers, as it has been reported that workload in real data centers can be typically divided into server-based applications and MapReduce applications with different performance and scheduling criteria, we provide separate resource management solutions for each type of workloads. For server-based applications, we provide a dynamic capacity provisioning scheme that dynamically adjusts the number of active servers to achieve the best trade-off between energy savings and scheduling delay, while considering heterogeneous resource characteristics of both workload and physical machines. For MapReduce applications, we first analyzed task run-time resource consumption of a large variety of MapReduce jobs and discovered it can vary significantly over-time, depending on the phase the task is currently executing. We then present a novel scheduling algorithm that controls task execution at the level of phases with the aim of improving both job running time and resource utilization. Through detailed simulations and experiments using real cloud clusters, we have found our proposed solutions achieve substantial gain compared to current state-of-art resource management solutions, and therefore have strong implications in the design of real cloud resource management systems in practice

Planning and Optimization During the Life-Cycle of Service Level Agreements for Cloud Computing

Ein Service Level Agreement (SLA) ist ein elektronischer Vertrag zwischen dem Kunden und dem Anbieter eines Services. Die beteiligten Partner kl aren ihre Erwartungen und Verp ichtungen in Bezug auf den Dienst und dessen Qualit at. SLAs werden bereits f ur die Beschreibung von Cloud-Computing-Diensten eingesetzt. Der Diensteanbieter stellt sicher, dass die Dienstqualit at erf ullt wird und mit den Anforderungen des Kunden bis zum Ende der vereinbarten Laufzeit ubereinstimmt. Die Durchf uhrung der SLAs erfordert einen erheblichen Aufwand, um Autonomie, Wirtschaftlichkeit und E zienz zu erreichen. Der gegenw artige Stand der Technik im SLA-Management begegnet Herausforderungen wie SLA-Darstellung f ur Cloud- Dienste, gesch aftsbezogene SLA-Optimierungen, Dienste-Outsourcing und Ressourcenmanagement. Diese Gebiete scha en zentrale und aktuelle Forschungsthemen. Das Management von SLAs in unterschiedlichen Phasen w ahrend ihrer Laufzeit erfordert eine daf ur entwickelte Methodik. Dadurch wird die Realisierung von Cloud SLAManagement vereinfacht. Ich pr asentiere ein breit gef achertes Modell im SLA-Laufzeitmanagement, das die genannten Herausforderungen adressiert. Diese Herangehensweise erm oglicht eine automatische Dienstemodellierung, sowie Aushandlung, Bereitstellung und Monitoring von SLAs. W ahrend der Erstellungsphase skizziere ich, wie die Modellierungsstrukturen verbessert und vereinfacht werden k onnen. Ein weiteres Ziel von meinem Ansatz ist die Minimierung von Implementierungs- und Outsourcingkosten zugunsten von Wettbewerbsf ahigkeit. In der SLA-Monitoringphase entwickle ich Strategien f ur die Auswahl und Zuweisung von virtuellen Cloud Ressourcen in Migrationsphasen. Anschlie end pr ufe ich mittels Monitoring eine gr o ere Zusammenstellung von SLAs, ob die vereinbarten Fehlertoleranzen eingehalten werden. Die vorliegende Arbeit leistet einen Beitrag zu einem Entwurf der GWDG und deren wissenschaftlichen Communities. Die Forschung, die zu dieser Doktorarbeit gef uhrt hat, wurde als Teil von dem SLA@SOI EU/FP7 integriertem Projekt durchgef uhrt (contract No. 216556)

Clustering algorithms for dynamic adaptation of service function chains

Network function virtualization is a pillar-stone of today’s network architectures as it offers better management and elasticity and allows also a flexible maintenance of services running on shared resources over cloud environments. Network functions traditionally hosted on dedicated hardware are now provided over software based components that might run either on virtual machines or on containers. The major advantage of this transition is that it makes the deployment of new services easier while optimizing the management and administration of network architectures. It is much easier to spin up a new virtual machine/container hosting a network function or a specific application described as a service function chain, than to deploy a new hardware based equipment and checking its compatibility with the rest of the architecture. With all the advantages that this new paradigm offers comes a set of challenges related mainly to: 1) optimizing the resource consumption on the shared infrastructure 2) making the best decision of placing the virtual functions that respects at the same time clients’ requirements and also leverages the available resources on the substrate network in terms of different metrics (e.g., CPU, memory, latency, bandwidth). This aspect of Network Function Virtualization-NFV and Service Function Chains-SFC placement have been treated in so many research works that propose approaches ensuring optimal placement and chaining of VNFs in virtualized networks, but as the adoption of these technologies gets more important in real network setups, and given the strict restrictions of today’s’ applications (e.g. latency highly-sensitive applications, or availability highly-sensitive service, etc.), it is always important to consider all the parameters impacting the network management in cloud environments. In this research project, we develop new approaches for placement and chaining of virtual network functions in cloud-based environments. The first approach allows forming on demand clusters of servers deployed in a physical infrastructure. These servers are grouped according to their similar attributes (e.g., CPU-intensive server, energy-efficient server, etc). This process is a proactive measure to ensure that SFCs are hosted in servers that meet their specific metrics requirements (CPU, memory, disk, etc.). It employs a meta-heuristic called CRO (Chemical Reaction Optimization) to decide of the best VNF placement guaranteeing optimal resource consumption in terms of CPU / memory. We employ CRO also to ensure the lowest latencies during the routing between the different VNFs. In fact, the E2E delay is an important aspect to consider, as most current applications require low latencies and shortest run times. In the second approach, the clusters are formed using algorithms based on meta-heuristics, including the CRO, allowing to improve the quality of clusters formed in terms of similarity, density and modularity

QoS-aware Cloud Infrastructure Provisioning in Heterogeneous Environments

Over the last decades Information Technology (IT) has become an enabler for nearly all businesses from industrial production to finance. The IT resources required for these business activities are usually provided by local and remote data centers. Although most resources are still hosted in companies’ proprietary data centers, cloud computing initiated a paradigm shift in IT service provisioning from owning to leasing resources and services. Today, over 50% of German companies use cloud services while shifting services into the cloud has become an emerging trend. Cloud computing, which is often referred to as the fifth utility in addition to water, electricity, gas, and telephony, provides commoditized computation resources that are available any time on demand in the required quantity. However, in contrast to other commodities, a single quality level is insufficient for IT service provisioning. Instead, the required quality for a provided IT service depends on the various functional and non-functional requirements. For example, highly interactive applications such as cloud gaming require a high quality level in terms of latency. Providers of cloud services have to face a highly competitive market. Cost advantages in cloud computing are primarily achieved by utilizing large centralized data centers at low-cost locations. However, this kind of resource provisioning impacts the quality of service of different types of services such as the aforementioned interactive multimedia services that possess strict quality of service constraints. Hence, infrastructure providers have to face a trade-off between cost reduction and adherence to the required Quality of Service (QoS) attributes. Apart from how services are provisioned, the way of consuming IT services also changed substantially over the last years. Mobile devices have begun to replace locally installed desktop computers at an accelerated pace. By utilizing these mobile devices, service providers are confronted with two major challenges: (i) a cellular network connection, which potentially causes a higher and more fluctuating latency and (ii) severely limited resources compared to local Personal Computers (PCs). These two aspects restrict the utilization of multimedia services, e. g., cloud gaming. To address these challenges, we present two novel approaches for (i) resource planning on a global level for multiple services with heterogeneous QoS characteristics and (ii) the augmentation of the centralized cloud infrastructure with locally installed resources to provide viable multimedia services to mobile devices. As the first major contribution, we introduce the Cloud Data Center Selection Problem (CDCSP). This problem describes the data center placement and resource selection on a global scale. We consider the role of a cloud provider, who aims to dimension resources in a cost-minimal fashion under the consideration of multiple services with different QoS attributes. Based on a mathematical optimization model, we propose the exact solution approach CDCSP-EXA.KOM. Due to the high complexity and the resulting computational effort to find the optimal solution, we propose and analyze four heuristic approaches to identify the most appropriate one for the given problem. As a first heuristic, we propose an approach based on linear program relaxation, CDCSP-REL.KOM. Furthermore, to take the specific structure of the problem into consideration, we develop the custom tailored CDCSP-PBST.KOM approach, which is based on a prioritized processing of demands and supplies. To further improve the results, we combine multiple heuristics to a Best-of-Breed approach, named CDCSP-BoB. KOM. Finally, as a metaheuristic improvement procedure, we propose the tabu search approach CDCSP-TS.KOM. To assess the practical applicability and performance of these optimization approaches, we analyze them in detail and compare their performance in a quantitatively. The second major contribution of this work addresses the augmentation of the centralized cloud infrastructure with local resources to provide services to mobile devices. Therefore, we formulate the Dynamic Cloudlet Placement and Selection Problem (DCPSP), as a multi-period resource planning problem, which includes local characteristics, such as space for hosting resources and available network bandwidth. We focus on a cloud provider who aims to augment the centralized infrastructure using local resources to improve the QoS guarantees for mobile used applications. We formalize the problem as a mathematical optimization model and derive the exact solution approach DCPSP-EXA.KOM. Due to the high complexity that is caused by an optimization over many time slots, we propose the heuristic optimization approach DCPSP-HEU.KOM. We assess the performance of these two approaches by the means of quantitative evaluation. In summary, the contributions of this thesis provide the means for a cost-efficient and QoS-aware resource selection in cloud infrastructures. We contribute the formalization of the problems and algorithms to support the efficient planning of future cloud infrastructures in environments with a multitude of heterogeneous services on a global scale. Furthermore, to enable mobile users to consume multimedia cloud services, we propose an optimization model and algorithms to augment a global centralized infrastructure by local resource units

Edge Intelligence Simulator:a platform for simulating intelligent edge orchestration solutions

Abstract. To support the stringent requirements of the future intelligent and interactive applications, intelligence needs to become an essential part of the resource management in the edge environment. Developing intelligent orchestration solutions is a challenging and arduous task, where the evaluation and comparison of the proposed solution is a focal point. Simulation is commonly used to evaluate and compare proposed solutions. However, there does not currently exist openly available simulators that would have a specific focus on supporting the research on intelligent edge orchestration methods. This thesis presents a simulation platform called Edge Intelligence Simulator (EISim), the purpose of which is to facilitate the research on intelligent edge orchestration solutions. In its current form, the platform supports simulating deep reinforcement learning based solutions and different orchestration control topologies in scenarios related to task offloading and resource pricing on edge. The platform also includes additional tools for creating simulation environments, running simulations for agent training and evaluation, and plotting results. This thesis gives a comprehensive overview of the state of the art in edge and fog simulation, orchestration, offloading, and resource pricing, which provides a basis for the design of EISim. The methods and tools that form the foundation of the current EISim implementation are also presented, along with a detailed description of the EISim architecture, default implementations, use, and additional tools. Finally, EISim with its default implementations is validated and evaluated through a large-scale simulation study with 24 simulation scenarios. The results of the simulation study verify the end-to-end performance of EISim and show its capability to produce sensible results. The results also illustrate how EISim can help the researcher in controlling and monitoring the training of intelligent agents, as well as in evaluating solutions against different control topologies.Reunaälysimulaattori : alusta älykkäiden reunalaskennan orkestrointiratkaisujen simulointiin. Tiivistelmä. Älykkäiden ratkaisujen täytyy tulla olennaiseksi osaksi reunaympäristön resurssien hallinnointia, jotta tulevaisuuden vuorovaikutteisten ja älykkäiden sovellusten suoritusta voidaan tukea tasolla, joka täyttää sovellusten tiukat suoritusvaatimukset. Älykkäiden orkestrointiratkaisujen kehitys on vaativa ja työläs prosessi, jonka keskiöön kuuluu olennaisesti menetelmien testaaminen ja vertailu muita menetelmiä vasten. Simulointia käytetään tyypillisesti menetelmien arviointiin ja vertailuun, mutta tällä hetkellä ei ole avoimesti saatavilla simulaattoreita, jotka eritoten keskittyisivät tukemaan älykkäiden reunaorkestrointiratkaisujen kehitystä. Tässä opinnäytetyössä esitellään simulaatioalusta nimeltään Edge Intelligence Simulator (EISim; Reunaälysimulaattori), jonka tarkoitus on helpottaa älykkäiden reunaorkestrointiratkaisujen tutkimusta. Nykymuodossaan se tukee vahvistusoppimispohjaisten ratkaisujen sekä erityyppisten orkestroinnin kontrollitopologioiden simulointia skenaarioissa, jotka liittyvät laskennan siirtoon ja resurssien hinnoitteluun reunaympäristössä. Alustan mukana tulee myös lisätyökaluja, joita voi käyttää simulaatioympäristöjen luomiseen, simulaatioiden ajamiseen agenttien koulutusta ja arviointia varten, sekä simulaatiotulosten visualisoimiseen. Tämä opinnäytetyö sisältää kattavan katsauksen reunaympäristön simuloinnin, reunaorkestroinnin, laskennan siirron ja resurssien hinnoittelun nykytilaan kirjallisuudessa, mikä tarjoaa kunnollisen lähtökohdan EISimin toteutukselle. Opinnäytetyö esittelee menetelmät ja työkalut, joihin EISimin tämänhetkinen toteutus perustuu, sekä antaa yksityiskohtaisen kuvauksen EISimin arkkitehtuurista, oletustoteutuksista, käytöstä ja lisätyökaluista. EISimin validointia ja arviointia varten esitellään laaja simulaatiotutkimus, jossa EISimin oletustoteutuksia simuloidaan 24 simulaatioskenaariossa. Simulaatiotutkimuksen tulokset todentavat EISimin kokonaisvaltaisen toimintakyvyn, sekä osoittavat EISimin kyvyn tuottaa järkeviä tuloksia. Tulokset myös havainnollistavat, miten EISim voi auttaa tutkijoita älykkäiden agenttien koulutuksessa ja ratkaisujen arvioinnissa eri kontrollitopologioita vasten

Energy and performance-optimized scheduling of tasks in distributed cloud and edge computing systems

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival and heterogeneity of tasks. This dissertation proposes a class of energy and performance-optimized scheduling algorithms built on top of several intelligent optimization algorithms. This dissertation includes two parts, including background work, i.e., Chapters 3–6, and new contributions, i.e., Chapters 7–11. 1) Background work of this dissertation. Chapter 3 proposes a spatial task scheduling and resource optimization method to minimize the total cost of CDCs where bandwidth prices of Internet service providers, power grid prices, and renewable energy all vary with locations. Chapter 4 presents a geography-aware task scheduling approach by considering spatial variations in CDCs to maximize the profit of their providers by intelligently scheduling tasks. Chapter 5 presents a spatio-temporal task scheduling algorithm to minimize energy cost by scheduling heterogeneous tasks among CDCs while meeting their delay constraints. Chapter 6 gives a temporal scheduling algorithm considering temporal variations of revenue, electricity prices, green energy and prices of public clouds. 2) Contributions of this dissertation. Chapter 7 proposes a multi-objective optimization method for CDCs to maximize their profit, and minimize the average loss possibility of tasks by determining task allocation among Internet service providers, and task service rates of each CDC. A simulated annealing-based bi-objective differential evolution algorithm is proposed to obtain an approximate Pareto optimal set. A knee solution is selected to schedule tasks in a high-profit and high-quality-of-service way. Chapter 8 formulates a bi-objective constrained optimization problem, and designs a novel optimization method to cope with energy cost reduction and QoS improvement. It jointly minimizes both energy cost of CDCs, and average response time of all tasks by intelligently allocating tasks among CDCs and changing task service rate of each CDC. Chapter 9 formulates a constrained bi-objective optimization problem for joint optimization of revenue and energy cost of CDCs. It is solved with an improved multi-objective evolutionary algorithm based on decomposition. It determines a high-quality trade-off between revenue maximization and energy cost minimization by considering CDCs’ spatial differences in energy cost while meeting tasks’ delay constraints. Chapter 10 proposes a simulated annealing-based bees algorithm to find a close-to-optimal solution. Then, a fine-grained spatial task scheduling algorithm is designed to minimize energy cost of CDCs by allocating tasks among multiple green clouds, and specifies running speeds of their servers. Chapter 11 proposes a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are met in cloud-edge computing systems. A single-objective constrained optimization problem is solved by a proposed simulated annealing-based migrating birds optimization. This dissertation evaluates these algorithms, models and software with real-life data and proves that they improve scheduling precision and cost-effectiveness of distributed cloud and edge computing systems