Design and Evaluation of Distributed Algorithms for Placement of Network Services

Network services play an important role in the Internet today. They serve as data caches for websites, servers for multiplayer games and relay nodes for Voice over IP: VoIP) conversations. While much research has focused on the design of such services, little attention has been focused on their actual placement. This placement can impact the quality of the service, especially if low latency is a requirement. These services can be located on nodes in the network itself, making these nodes supernodes. Typically supernodes are selected in either a proprietary or ad hoc fashion, where a study of this placement is either unavailable or unnecessary. Previous research dealt with the only pieces of the problem, such as finding the location of caches for a static topology, or selecting better routes for relays in VoIP. However, a comprehensive solution is needed for dynamic applications such as multiplayer games or P2P VoIP services. These applications adapt quickly and need solutions based on the immediate demands of the network. In this thesis we develop distributed algorithms to assign nodes the role of a supernode. This research first builds off of prior work by modifying an existing assignment algorithm and implementing it in a distributed system called Supernode Placement in Overlay Topologies: SPOT). New algorithms are developed to assign nodes the supernode role. These algorithms are then evaluated in SPOT to demonstrate improved SN assignment and scalability. Through a series of simulation, emulation, and experimentation insight is gained into the critical issues associated with allocating resources to perform the role of supernodes. Our contributions include distributed algorithms to assign nodes as supernodes, an open source fully functional distributed supernode allocation system, an evaluation of the system in diverse networking environments, and a simulator called SPOTsim which demonstrates the scalability of the system to thousands of nodes. An example of an application deploying such a system is also presented along with the empirical results

Evaluating DHT-Based Service Placement for Stream Based Overlays

Stream-based overlay networks (SBONs) are one approach to implementing large-scale stream processing systems. A fundamental consideration in an SBON is that of service placement, which determines the physical location of in-network processing services or operators, in such a way that network resources are used efficiently. Service placement consists of two components: node discovery, which selects a candidate set of nodes on which services might be placed, and node selection, which chooses the particular node to host a service. By viewing the placement problem as the composition of these two processes we can trade-off quality and efficiency between them. A bad discovery scheme can yield a good placement, but at the cost of an expensive selection mechanism. Recent work on operator placement [3, 9] proposes to leverage routing paths in a distributed hash table (DHT) to obtain a set of candidate nodes for service placement. We evaluate the appropriateness of using DHT routing paths for service placement in an SBON, when aiming to minimize network usage. For this, we consider two DHT-based algorithms for node discovery, which use either the union or intersection of DHT routing paths in the SBON, and compare their performance to other techniques. We show that current DHT-based schemes are actually rather poor node discovery algorithms, when minimizing network utilization. An efficient DHT may not traverse enough hops to obtain a sufficiently large candidate set for placement. The union of DHT routes may result in a low-quality set of discovered nodes that requires an expensive node selection algorithm. Finally, the intersection of DHT routes relies on route convergence, which prevents the placement of services with a large fan-in.Engineering and Applied Science

Management And Security Of Multi-Cloud Applications

Single cloud management platform technology has reached maturity and is quite successful in information technology applications. Enterprises and application service providers are increasingly adopting a multi-cloud strategy to reduce the risk of cloud service provider lock-in and cloud blackouts and, at the same time, get the benefits like competitive pricing, the flexibility of resource provisioning and better points of presence. Another class of applications that are getting cloud service providers increasingly interested in is the carriers\u27 virtualized network services. However, virtualized carrier services require high levels of availability and performance and impose stringent requirements on cloud services. They necessitate the use of multi-cloud management and innovative techniques for placement and performance management. We consider two classes of distributed applications – the virtual network services and the next generation of healthcare – that would benefit immensely from deployment over multiple clouds. This thesis deals with the design and development of new processes and algorithms to enable these classes of applications. We have evolved a method for optimization of multi-cloud platforms that will pave the way for obtaining optimized placement for both classes of services. The approach that we have followed for placement itself is predictive cost optimized latency controlled virtual resource placement for both types of applications. To improve the availability of virtual network services, we have made innovative use of the machine and deep learning for developing a framework for fault detection and localization. Finally, to secure patient data flowing through the wide expanse of sensors, cloud hierarchy, virtualized network, and visualization domain, we have evolved hierarchical autoencoder models for data in motion between the IoT domain and the multi-cloud domain and within the multi-cloud hierarchy

Extensible Signaling Framework for Decentralized Network Management Applications

The management of network infrastructures has become increasingly complex over time, which is mainly attributed to the introduction of new functionality to support emerging services and applications. To address this important issue, research efforts in the last few years focused on developing Software-Defined Networking solutions. While initial work proposed centralized architectures, their scalability limitations have led researchers to investigate a distributed control plane, with controller placement algorithms and mechanisms for building a logically centralized network view, being examples of challenges addressed. A critical issue that has not been adequately addressed concerns the communication between distributed decision-making entities to ensure configuration consistency. To this end, this paper proposes a signaling framework that can allow the exchange of information in distributed management and control scenarios. The benefits of the proposed framework are illustrated through a realistic network resource management use case. Based on simulation, we demonstrate the flexibility and extensibility of our solution in meeting the requirements of distributed decision-making processes

Resource Allocation in Distributed Service Networks

The past few years have witnessed significant growth in the use of distributed network analytics involving agile code, data and computational resources. In many such networked systems, for example, Internet of Things (IoT), a large number of smart devices, sensors, processing and storage resources are widely distributed in a geographic region. These devices and resources distributed over a physical space are collectively called a distributed service network. Efficient resource allocation in such high performance service networks remains one of the most critical problems. In this thesis, we model and optimize the allocation of resources in a distributed service network. This thesis contributes to two different types of service networks: caching, and spatial networks; and develops new techniques that optimize the overall performance of these services. First, we propose a new method to compute an upper bound on hit probability for all non-anticipative caching policies in a distributed caching system. We find our bound to be tighter than state-of-the-art upper bounds for a variety of content request arrival processes. We then develop a utility based framework for content placement in a cache network for efficient and fair allocation of caching resources. We develop provably optimal distributed algorithms that operate at each network cache to maximize the overall network utility. Next, we develop and evaluate assignment policies that allocate resources to users with a goal to minimize the expected distance traveled by a user request, where both resources and users are located on a line. Lastly, we design and evaluate resource proximity aware user-request allocation policies with a goal to reduce the implementation cost associated with moving a request/job to/from its allocated resource while balancing the number of requests allocated to a resource. Depending on the topology, our proposed policies achieve a 8% - 99% decrease in implementation cost as compared to the state-of-the-art

Dynamic resource allocation for virtual network function placement in satellite edge clouds

Satellite edge computing has become a promising way to provide computing services for Internet of Things (IoT) users in remote areas, which are out of the coverage of terrestrial networks. Nevertheless, it is not suitable for large-scale IoT users due to the resource limitation of satellites. Cloud computing can provide sufficient available resources for IoT users, but it does not meet delay-sensitive services as high network latency. Satellite edge clouds can facilitate flexible service provisioning for numerous IoT users by incorporating the advantages of edge computing and cloud computing. In this paper, we investigate the dynamic resource allocation problem for virtual network function (VNF) placement in satellite edge clouds. The aim is to minimize the network bandwidth cost and the service end-to-end delay jointly. We formulate the VNF placement problem as an integer non-linear programming problem and then propose a distributed VNF placement (D-VNFP) algorithm to address it. The experiments are conducted to evaluate the performance of the proposed D-VNFP algorithm, where Viterbi and Game theory are considered as the baseline algorithms. The results show that the proposed D-VNFP algorithm is effective and efficient for solving the VNF placement problem in satellite edge clouds

On the Optimality of Virtualized Security Function Placement in Multi-Tenant Data Centers

Security and service protection against cyber attacks remain among the primary challenges for virtualized, multi-tenant Data Centres (DCs), for reasons that vary from lack of resource isolation to the monolithic nature of legacy middleboxes. Although security is currently considered a property of the underlying infrastructure, diverse services require protection against different threats and at timescales which are on par with those of service deployment and elastic resource provisioning. We address the resource allocation problem of deploying customised security services over a virtualized, multi-tenant DC. We formulate the problem in Integral Linear Programming (ILP) as an instance of the NP-hard variable size variable cost bin packing problem with the objective of maximising the residual resources after allocation. We propose a modified version of the Best Fit Decreasing algorithm (BFD) to solve the problem in polynomial time and we show that BFD optimises the objective function up to 80% more than other algorithms