application cost-aware cloud provisioning

Οι πλατφόρμες νέφους επιτρέπουν στους ιδιοκτήτες εφαρμογών την ενοικίαση πόρων, προκειμένου να επεκτείνουν δυναμικά τη συνολική υπολογιστική ισχύ των υποδομών τους. Τα χαρακτηριστικά και οι τιμές των πόρων αυτών συνήθως ποικίλουν. Οι πάροχοι νέφους διασφαλίζουν την ποιότητα υπηρεσίας μέσω εγγυήσεων (Service Layer Agreements) και πληρώνουν ποινή όταν μια εγγύηση παραβιάζεται. Συνηθως, οι βασισμένες στο νέφος εφαρμογές να προσφέρουν και αυτές τέτοιες εγγυήσεις στους χρήστες. Σε ένα δυναμικό περιβάλλον, όπου ο χρήστης εκτελεί εφαρμογές στο ιδιωτικό νέφος και μπορούν να προσθαφαιρούν κόμβους από πάροχους (δημόσιου) νέφους 2 διαφορετικά είδη SLAs υπάρχουν (i) το SLA που προσφέρεται από την εφαρμογή στους τελικούς χρήστες και (ii) το SLA που προσφέρεται από τους παρόχους νέφους στην εφαρμογή. Έτσι, μια ποινή για παραβίαση SLA από την εφαρμογή στους τελικούς χρήστες μπορεί να είναι χαμηλότερη αν παραβιάζεται και το SLA του παρόχου δημοσίου νέφους. Αυτή η ιδιότητα καθιστά τον υπολογισμό του συνολικού κόστους λειτουργίας περίπλοκο αλλά επεκτείνει το χώρο αναζήτησης των επιλογών με το χαμηλότερο συνολικό κόστος. Σε αυτήν τη διπλωματική εργασία παρουσιάζουμε έναν αλγόριθμο παροχής πόρων NoSQL εφαρμογών, που στοχεύει στην ελαχιστοποίηση του συνολικού κόστους της εφαρμογής λαμβάνοντας υπόψη τις ιδιότητες ελαστικότητας της εφαρμογής αυτής σε ένα ετερογενές περιβάλλον και είναι βασισμένος σε ‘‘look-ahead’’ βελτιστοποίησηCloud computing platforms allow application owners to rent resources in order to expand dynamically the overall computational power of their infrastructure. The resources characteristics and lease prices usually vary. Cloud providers ensure the Quality of Service through Service Layer Agreements (SLAs) and pay a penalty when these agreements are violated. Usually, cloud-based applications also offer SLAs to the users. In a dynamic environment, where a user is running applications on her private cloud and add/remove nodes from (public) cloud providers, 2 types of SLAs exist (i) the SLA offered by the application to the end users and (ii) the SLA offered by the cloud providers to the application. Thus, a penalty for an SLA violation from the application to the end users might be lower if the SLA from the public cloud provider is also violated. This property makes the calculation of the total operational cost complex, but also expands the search space of choices with lower total cost. In this thesis we present an application-cost aware resource provisioning algorithm for NoSQL applications that aims to minimize the application total cost by taking into account the elasticity properties of that application in a heterogeneous environment and is based on look-ahead optimization

A Multi-Objective Load Balancing System for Cloud Environments

© 2017 The British Computer Society. All rights reserved. Virtual machine (VM) live migration has been applied to system load balancing in cloud environments for the purpose of minimizing VM downtime and maximizing resource utilization. However, the migration process is both time-and cost-consuming as it requires the transfer of large size files or memory pages and consumes a huge amount of power and memory for the origin and destination physical machine (PM), especially for storage VM migration. This process also leads to VM downtime or slowdown. To deal with these shortcomings, we develop a Multi-objective Load Balancing (MO-LB) system that avoids VM migration and achieves system load balancing by transferring extra workload from a set of VMs allocated on an overloaded PM to other compatible VMs in the cluster with greater capacity. To reduce the time factor even more and optimize load balancing over a cloud cluster, MO-LB contains a CPU Usage Prediction (CUP) sub-system. The CUP not only predicts the performance of the VMs but also determines a set of appropriate VMs with the potential to execute the extra workload imposed on the VMs of an overloaded PM. We also design a Multi-Objective Task Scheduling optimization model using Particle Swarm Optimization to migrate the extra workload to the compatible VMs. The proposed method is evaluated using a VMware-vSphere-based private cloud in contrast to the VM migration technique applied by vMotion. The evaluation results show that the MO-LB system dramatically increases VM performance while reducing service response time, memory usage, job makespan, power consumption and the time taken for the load balancing process

Auto-scaling techniques for cloud-based Complex Event Processing

One key topic in cloud computing is elasticity, which is the ability of the cloud environment to timely adapt the resource assignment along with the workload demand. According to cloud on-demand model, the infrastructure should be able to scale up and down to unpredictable workloads, in order to achieve both a guaranteed service level and cost efficiency. This work addresses the cloud elasticity problem, with particular reference to the Complex Event Processing (CEP) systems. CEP systems are designed to process large volumes of event-driven data streams and continuously provide results with a low latency and in real-time. CEP systems need to adapt to changing query and events loads. Because of the high computational requirements and varying loads, CEP are distributed system and running on cloud infrastructures. In this work we review the cloud computing auto-scaling solutions, and study their suit- ability in the CEP model. We implement some solutions in a CEP prototype and evaluate the experimental results

RAMP: RDMA Migration Platform

Remote Direct Memory Access (RDMA) can be used to implement a shared storage abstraction or a shared-nothing abstraction for distributed applications. We argue that the shared storage abstraction is overkill for loosely coupled applications and that the shared-nothing abstraction does not leverage all the benefits of RDMA. In this thesis, we propose an alternative abstraction for such applications using a shared-on-demand architecture, and present the RDMA Migration Platform (RAMP). RAMP is a lightweight coordination service for building loosely coupled distributed applications. This thesis describes the RAMP system, its programming model and operations, and evaluates the performance of RAMP using microbenchmarks. Furthermore, we illustrate RAMPs load balancing capabilities with a case study of a loosely coupled application that uses RAMP to balance a partition skew under load

Elastic Resource Management in Distributed Clouds

The ubiquitous nature of computing devices and their increasing reliance on remote resources have driven and shaped public cloud platforms into unprecedented large-scale, distributed data centers. Concurrently, a plethora of cloud-based applications are experiencing multi-dimensional workload dynamics---workload volumes that vary along both time and space axes and with higher frequency. The interplay of diverse workload characteristics and distributed clouds raises several key challenges for efficiently and dynamically managing server resources. First, current cloud platforms impose certain restrictions that might hinder some resource management tasks. Second, an application-agnostic approach might not entail appropriate performance goals, therefore, requires numerous specific methods. Third, provisioning resources outside LAN boundary might incur huge delay which would impact the desired agility. In this dissertation, I investigate the above challenges and present the design of automated systems that manage resources for various applications in distributed clouds. The intermediate goal of these automated systems is to fully exploit potential benefits such as reduced network latency offered by increasingly distributed server resources. The ultimate goal is to improve end-to-end user response time with novel resource management approaches, within a certain cost budget. Centered around these two goals, I first investigate how to optimize the location and performance of virtual machines in distributed clouds. I use virtual desktops, mostly serving a single user, as an example use case for developing a black-box approach that ranks virtual machines based on their dynamic latency requirements. Those with high latency sensitivities have a higher priority of being placed or migrated to a cloud location closest to their users. Next, I relax the assumption of well-provisioned virtual machines and look at how to provision enough resources for applications that exhibit both temporal and spatial workload fluctuations. I propose an application-agnostic queueing model that captures the resource utilization and server response time. Building upon this model, I present a geo-elastic provisioning approach---referred as geo-elasticity---for replicable multi-tier applications that can spin up an appropriate amount of server resources in any cloud locations. Last, I explore the benefits of providing geo-elasticity for database clouds, a popular platform for hosting application backends. Performing geo-elastic provisioning for backend database servers entails several challenges that are specific to database workload, and therefore requires tailored solutions. In addition, cloud platforms offer resources at various prices for different locations. Towards this end, I propose a cost-aware geo-elasticity that combines a regression-based workload model and a queueing network capacity model for database clouds. In summary, hosting a diverse set of applications in an increasingly distributed cloud makes it interesting and necessary to develop new, efficient and dynamic resource management approaches

EXPLOITING THE SYNERGY BETWEEN SCHEDULING AND LOAD SHEDDING TO FACILITATE DIFFERENTIATED LEVELS OF SERVICE FOR CONTINUOUS QUERIES

Data Stream Management Systems (DSMSs) offer the most effective solution for processing data streams by efficiently executing continuous queries (CQs) over the incoming data. CQs inherently have different levels of criticality and hence different levels of expected quality of service (QoS) and quality of data (QoD). Adhering to such expected QoS/QoD metrics is even more important in cases of multi-tenant data stream management services. In this dissertation, we propose DILoS, a framework that supports differentiated QoS and QoD for multiple classes of CQs by tightly integrating priority-based scheduling and load shedding. Unlike existing works that consider scheduling and load shedding separately, DILoS is a novel unified framework that exploits the synergy between them. For the realization of DILoS, we propose ALoMa and SEaMLeSS, two general, adaptive load managers. Our load managers can also be used standalone and outperform the state-of-the-art in three dimensions: (1)they automatically tune the headroom factor, (2) they honor the delay target, and (3) they are applicable to complex query networks with shared operators. We implemented DILoS, ALoMa and SEaMLeSS in our real DSMS prototype system (AQSIOS) and systematically evaluate their performance using real and synthetic workloads.Our experimental evaluation of ALoMa and SEaMLeSS verified their advantages over the state-of-the-art approaches. Our evaluation of DILoS showed that it (a) allows the scheduler and load shedder to consistently honor CQs’ priorities, (b) significantly increases system capacity utilization by exploiting batch processing, and (c) enables operator sharing among query classes of different priorities while avoiding priority inversion. To further support differentiated QoS and QoD for CQs in distributed DSMSs, we propose ARMaDILoS, a conceptual framework for large scale adaptive resource management using DILoS. A fundamental component in ARMaDILoS is CQ migration. For this reason, we propose and implement UniMiCo, a protocol to migrate CQs without interrupting the execution of the queries. Our experiments showed that UniMiCo produced correct outputs and did not introduce any hiccup in the response time of the queries

Security risk assessment in cloud computing domains

Cyber security is one of the primary concerns persistent across any computing platform. While addressing the apprehensions about security risks, an infinite amount of resources cannot be invested in mitigation measures since organizations operate under budgetary constraints. Therefore the task of performing security risk assessment is imperative to designing optimal mitigation measures, as it provides insight about the strengths and weaknesses of different assets affiliated to a computing platform. The objective of the research presented in this dissertation is to improve upon existing risk assessment frameworks and guidelines associated to different key assets of Cloud computing domains - infrastructure, applications, and users. The dissertation presents various informal approaches of performing security risk assessment which will help to identify the security risks confronted by the aforementioned assets, and utilize the results to carry out the required cost-benefit tradeoff analyses. This will be beneficial to organizations by aiding them in better comprehending the security risks their assets are exposed to and thereafter secure them by designing cost-optimal mitigation measures --Abstract, page iv