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A Game Theoretic Formulation of the Service Provisioning Problem in Cloud Systems
"... Cloud computing is an emerging paradigm which allows the on-demand delivering of software, hardware, and data as services. As cloud-based services are more numerous and dynamic, the development of efficient service provisioning policies become increasingly challenging. Game theoretic approaches have ..."
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Cloud computing is an emerging paradigm which allows the on-demand delivering of software, hardware, and data as services. As cloud-based services are more numerous and dynamic, the development of efficient service provisioning policies become increasingly challenging. Game theoretic approaches have shown to gain a thorough analytical understanding of the service provisioning problem. In this paper we take the perspective of Software as a Service (SaaS) providers which host their applications at an Infrastructure as a Service (IaaS) provider. Each SaaS needs to comply with quality of service requirements, specified in Service Level Agreement (SLA) contracts with the end-users, which determine the revenues and penalties on the basis of the achieved performance level. SaaS providers want to maximize their revenues from SLAs, while minimizing the cost of use of resources supplied by the IaaS provider. Moreover, SaaS providers compete and bid for the use of infrastructural resources. On the other hand, the IaaS wants to maximize the revenues obtained providing virtualized resources. In this paper we model the service provisioning problem as a Generalized Nash game, and we propose an efficient algorithm for the run time management and allocation of IaaS resources to competing SaaSs.
Distributed dynamic speed scaling
"... Abstract — In recent years we have witnessed a great interest in large distributed computing platforms, also known as clouds. While these systems offer enormous computing power, they are however major energy consumers. In the existing data centers CPUs are responsible for approximately half of the e ..."
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Abstract — In recent years we have witnessed a great interest in large distributed computing platforms, also known as clouds. While these systems offer enormous computing power, they are however major energy consumers. In the existing data centers CPUs are responsible for approximately half of the energy consumed by the servers. A promising technique for saving CPU energy consumption is dynamic speed scaling, in which the speed at which the processor is ran is adjusted based on demand and performance constraints. In this paper we look at the problem of allocating the demand in the network of processors (each being capable to perform dynamic speed scaling) to minimize the global energy consumption/cost. The nonlinear dependence between the energy consumption and the performance as well as the high variability in the energy prices result in a nontrivial resource allocation. The problem can be abstracted as a fully distributed convex optimization with a linear constraint. On the theoretical side, we propose two low-overhead fully decentralized algorithms for solving the problem of interest and provide closedform conditions that ensure stability of the algorithms. Then we evaluate the efficacy of the optimal solution using simulations driven by the real-world energy prices. Our findings indicate a possible cost reduction of 10 − 40 % compared to power-oblivious 1/N load balancing, for a wide range of load factors. I.
Price of Anarchy in Non-Cooperative Load Balancing
"... We investigate the price of anarchy of a load balancing game with K dispatchers. The service rates and holding costs are assumed to depend on the server, and the service discipline is assumed to be processor-sharing at each server. The performance criterion is taken to be the weighted mean number of ..."
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We investigate the price of anarchy of a load balancing game with K dispatchers. The service rates and holding costs are assumed to depend on the server, and the service discipline is assumed to be processor-sharing at each server. The performance criterion is taken to be the weighted mean number of jobs in the system, or equivalently, the weighted mean sojourn time in the system. Independently of the state of the servers, each dispatcher seeks to determine the routing strategy that optimizes the performance for its own traffic. The interaction of the various dispatchers thus gives rise to a non-cooperative game. For this game, we first show that, for a fixed amount of total incoming traffic, the worst-case Nash equilibrium occurs when each player routes exactly the same amount of traffic, i.e., when the game is symmetric. For this symmetric game, we provide the expression for the loads on the servers at the Nash equilibrium. Using this result we then show that, for a system with two or more servers, the price of anarchy, which is the worst-case ratio of the global cost of the Nash equilibrium to the global cost of the centralized setting, is lower bounded by K/(2 √ K − 1) and upper bounded by √ K, independently of the number of servers. Keywords: atomic games, load balancing, processor sharing, price of anarchy.
Competition yields efficiency in load balancing games
"... We study a nonatomic congestion game with N parallel links, with each link under the control of a profit maximizing provider. Within this ‘load balancing game’, each provider has the freedom to set a price, or toll, for access to the link and seeks to maximize its own profit. Given prices, a Wardrop ..."
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We study a nonatomic congestion game with N parallel links, with each link under the control of a profit maximizing provider. Within this ‘load balancing game’, each provider has the freedom to set a price, or toll, for access to the link and seeks to maximize its own profit. Given prices, a Wardrop equilibria among users is assumed, under which users all choose paths of minimal cost, which is defined as latency plus price. Within this model, we have oligopolistic price competition whichinequilibriumgives rise to situations where neither providers nor users have incentives to adjust their prices or routes, respectively. In this context, we provide new results about existence and efficiency of oligopolistic equilibria. Our main theorem shows that, when the number of providers is small, oligopolistic equilibria can be extremely inefficient; however as the number of providers N grows, the oligopolistic equilibria become increasingly efficient (at a rate of 1/N) and, as N → ∞, the oligopolistic equilibrium matches the socially optimal allocation. 1.
A Fair Scheduling for
- IEEE 802.16 Broadband Wireless Access Systems”, ICC2005, May 16-20, Souel, Kerea
"... the impact of heterogeneity and back-end ..."
Optimal file splitting for wireless networks with concurrent access
- Lecture Notes in Computer Science
, 2009
"... Abstract. The fundamental limits on channel capacity form a barrier to the sustained growth on the use of wireless networks. To cope with this, multi-path communication solutions provide a promising means to improve reliability and boost Quality of Service (QoS) in areas that are covered by a multit ..."
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Abstract. The fundamental limits on channel capacity form a barrier to the sustained growth on the use of wireless networks. To cope with this, multi-path communication solutions provide a promising means to improve reliability and boost Quality of Service (QoS) in areas that are covered by a multitude of wireless access networks. Today, little is known about how to effectively exploit this potential. Motivated by this, we consider N parallel communication networks, each of which is modeled as a processor sharing (PS) queue that handles two types of traffic: foreground and background. We consider a foreground traffic stream of files, each of which is split into N fragments according to a fixed splitting rule (α1,..., αN), where P αi = 1 and αi ≥ 0 is the fraction of the file that is directed to network i. Upon completion of transmission of all fragments of a file, it is re-assembled at the receiving
On the Interaction between Load Balancing and Speed Scaling
"... Abstract — Speed scaling has been widely adopted in computer and communication systems, in particular, to reduce energy consumption. An important question is how speed scaling interacts with other resource allocation mechanisms such as scheduling and routing, etc. In this paper, we study the interac ..."
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Abstract — Speed scaling has been widely adopted in computer and communication systems, in particular, to reduce energy consumption. An important question is how speed scaling interacts with other resource allocation mechanisms such as scheduling and routing, etc. In this paper, we study the interaction of speed scaling with load balancing. We characterize the equilibrium resulting from the load balancing and speed scaling interaction, and introduce two optimal load balancing designs, in terms of traditional performance metric and cost-aware (in particular, energy-aware) performance metric respectively. Especially, we characterize the load-balancing-speed-scaling equilibrium with respect to the optimal load balancing schemes in processor sharing systems. Our results show that the degree of inefficiency at the equilibrium is mostly bounded by the heterogeneity of the system, but independent of the number of the servers. These results provide insights in understanding the interaction of load balancing with speed scaling and guiding new designs.
Sub-modularity of waterfilling with applications to online basestation allocation,” arXiv preprint arXiv:1402.4892
, 2014
"... Abstract—We show that the popular water-filling algorithm for maximizing the mutual information in parallel Gaussian channels is sub-modular. The sub-modularity of water-filling algorithm is then used to derive online basestation allocation algorithms, where mobile users are assigned to one of many ..."
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Abstract—We show that the popular water-filling algorithm for maximizing the mutual information in parallel Gaussian channels is sub-modular. The sub-modularity of water-filling algorithm is then used to derive online basestation allocation algorithms, where mobile users are assigned to one of many possible basestations immediately and irrevocably upon arrival without knowing the future user information. The goal of the allocation is to maximize the sum-rate of the system under power allocation at each basestation. We present online algorithms with competitive ratio of at most 2 when compared to offline algorithms that have knowledge of all future user arrivals. I.
Optimal file splitting for wireless networks with concurrent access
"... Abstract. The fundamental limits on channel capacity form a barrier to the sustained growth on the use of wireless networks. To cope with this, multi-path communication solutions provide a promising means to improve reliability and boost Quality of Service (QoS) in areas that are covered by a multi ..."
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Abstract. The fundamental limits on channel capacity form a barrier to the sustained growth on the use of wireless networks. To cope with this, multi-path communication solutions provide a promising means to improve reliability and boost Quality of Service (QoS) in areas that are covered by a multitude of wireless access networks. Today, little is known about how to effectively exploit this potential. Motivated by this, we consider N parallel communication networks, each of which is modeled as a processor sharing (PS) queue that handles two types of traffic: foreground and background. We consider a foreground traffic stream of files, each of which is split into N fragments according to a fixed splitting rule (α1, . . . , αN ), where P αi = 1 and αi ≥ 0 is the fraction of the file that is directed to network i. Upon completion of transmission of all fragments of a file, it is re-assembled at the receiving end. The background streams use dedicated networks without being split. We study the sojourn time tail behavior of the foreground traffic. For the case of light foreground traffic and regularly varying foreground filesize distributions, we obtain a reduced-load approximation (RLA) for the sojourn times, similar to that of a single PS-queue. An important implication of the RLA is that the tail-optimal splitting rule is simply to choose αi proportional to ci − ρi, where ci is the capacity of network i and ρi is the load offered to network i by the corresponding background stream. This result provides a theoretical foundation for the effectiveness of such a simple splitting rule. Extensive simulations demonstrate that this simple rule indeed performs well, not only with respect to the tail asymptotics, but also with respect to the mean sojourn times. The simulations further support our conjecture that the same splitting rule is also tail-optimal for non-light foreground traffic. Finally, we observe near-insensitivity of the mean sojourn times with respect to the file-size distribution.
