Autonomous Demand Side Management Based on Energy Consumption Scheduling and Instantaneous Load Billing: An Aggregative Game Approach

In this paper, we investigate a practical demand side management scenario where the selfish consumers compete to minimize their individual energy cost through scheduling their future energy consumption profiles. We propose an instantaneous load billing scheme to effectively convince the consumers to shift their peak-time consumption and to fairly charge the consumers for their energy consumption. For the considered DSM scenario, an aggregative game is first formulated to model the strategic behaviors of the selfish consumers. By resorting to the variational inequality theory, we analyze the conditions for the existence and uniqueness of the Nash equilibrium (NE) of the formulated game. Subsequently, for the scenario where there is a central unit calculating and sending the real-time aggregated load to all consumers, we develop a one timescale distributed iterative proximal-point algorithm with provable convergence to achieve the NE of the formulated game. Finally, considering the alternative situation where the central unit does not exist, but the consumers are connected and they would like to share their estimated information with others, we present a distributed agreement-based algorithm, by which the consumers can achieve the NE of the formulated game through exchanging information with their immediate neighbors.Comment: 11 pages, 7 figure

A Literature Survey of Cooperative Caching in Content Distribution Networks

Content distribution networks (CDNs) which serve to deliver web objects (e.g., documents, applications, music and video, etc.) have seen tremendous growth since its emergence. To minimize the retrieving delay experienced by a user with a request for a web object, caching strategies are often applied - contents are replicated at edges of the network which is closer to the user such that the network distance between the user and the object is reduced. In this literature survey, evolution of caching is studied. A recent research paper [15] in the field of large-scale caching for CDN was chosen to be the anchor paper which serves as a guide to the topic. Research studies after and relevant to the anchor paper are also analyzed to better evaluate the statements and results of the anchor paper and more importantly, to obtain an unbiased view of the large scale collaborate caching systems as a whole.Comment: 5 pages, 5 figure

Proactive Resource Allocation: Harnessing the Diversity and Multicast Gains

This paper introduces the novel concept of proactive resource allocation through which the predictability of user behavior is exploited to balance the wireless traffic over time, and hence, significantly reduce the bandwidth required to achieve a given blocking/outage probability. We start with a simple model in which the smart wireless devices are assumed to predict the arrival of new requests and submit them to the network T time slots in advance. Using tools from large deviation theory, we quantify the resulting prediction diversity gain} to establish that the decay rate of the outage event probabilities increases with the prediction duration T. This model is then generalized to incorporate the effect of the randomness in the prediction look-ahead time T. Remarkably, we also show that, in the cognitive networking scenario, the appropriate use of proactive resource allocation by the primary users improves the diversity gain of the secondary network at no cost in the primary network diversity. We also shed lights on multicasting with predictable demands and show that the proactive multicast networks can achieve a significantly higher diversity gain that scales super-linearly with T. Finally, we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive (non-causal) wireless networking framework

Selfish grids: Game-theoretic modeling and NAS/PSA benchmark evaluation

Selfish behaviors of individual machines in a Grid can potentially damage the performance of the system as a whole. However, scrutinizing the Grid by taking into account the noncooperativeness of machines is a largely unexplored research problem. In this paper, we first present a new hierarchical game-theoretic model of the Grid that matches well with the physical administrative structure in real-life situations. We then focus on the impact of selfishness in intrasite job execution mechanisms. Based on our novel utility functions, we analytically derive the Nash equilibrium and optimal strategies for the general case. To study the effects of different strategies, we have also performed extensive simulations by using a well-known practical scheduling algorithm over the NAS (Numerical Aerodynamic Simulation) and the PSA (Parameter Sweep Application) workloads. We have studied the overall job execution performance of the Grid system under a wide range of parameters. Specifically, we find that the Optimal selfish strategy significantly outperforms the Nash selfish strategy. Our performance evaluation results can serve as a valuable reference for designing appropriate strategies in a practical Grid. © 2007 IEEE.published_or_final_versio

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial