Ammar Gharaibeh

A Provably Efficient Online Collaborative Caching Algorithm for Multicell-Coordinated Systems

Caching at the base stations brings the contents closer to the users, reduces the traffic through the backhaul links, and reduces the delay experienced by the cellular users. The cellular network operator may charge the content providers for caching their contents. Moreover, content providers may lose their users if the users are not getting their desired quality of service, such as maximum tolerable delay in Video on Demand services. In this paper, we study the collaborative caching problem for a multicell-coordinated system from the point of view of minimizing the total cost paid by the content providers. We formulate the problem as an Integer Linear Program and prove its NP-completeness. We also provide an online caching algorithm that does not require any knowledge about the contents popularities. We prove that the online algorithm achieves a competitive ratio of O(log (n)), and we show that the best competitive ratio that any online algorithm can achieve is Ω (log (n) / log log (n)). Therefore, our proposed caching algorithm is provably efficient. Through simulations, we show that our online algorithm performs very close to the optimal offline collaborative scheme, and can outperform it when contents popularities are not properly estimated.

Smart Cities: A Survey on Data Management, Security, and Enabling Technologies

Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. The IoT will generate tremendous amount of data that can be leveraged for safety, efficiency, and infotainment applications and services for city residents. The management of this voluminous data through its lifecycle is fundamental to the realization of smart cities. Therefore, in contrast to existing surveys on smart cities we provide a data-centric perspective, describing the fundamental data management techniques employed to ensure consistency, interoperability, granularity, and reusability of the data generated by the underlying IoT for smart cities. Essentially, the data lifecycle in a smart city is dependent on tightly coupled data management with cross-cutting layers of data security and privacy, and supporting infrastructure. Therefore, we further identify techniques employed for data security and privacy, and discuss the networking and computing technologies that enable smart cities. We highlight the achievements in realizing various aspects of smart cities, present the lessons learned, and identify limitations and research challenges.

Joint Caching, Routing, and Channel Assignment for Collaborative Small-Cell Cellular Networks

We consider joint caching, routing, and channel assignment for video delivery over coordinated small-cell cellular systems of the future Internet. We formulate the problem of maximizing the throughput of the system as a linear program, in which the number of variables is very large. To address channel interference, our formulation incorporates the conflict graph that arises when wireless links interfere with each other due to simultaneous transmission. We utilize the column generation method to solve the problem by breaking it into a restricted master subproblem that involves a select subset of variables and a collection of pricing subproblems that select the new variable to be introduced into the restricted master problem, if that leads to a better objective function value. To control the complexity of the column generation optimization further, due to the exponential number of independent sets that arise from the conflict graph, we introduce an approximation algorithm that computes a solution that is within ϵ to optimality, at much lower complexity. Our framework demonstrates considerable gains in average transmission rate at which the video data can be delivered to the users, over the state-of-the-art Femtocaching system, of up to 46%. These operational gains in system performance map to analogous gains in video application quality, thereby enhancing the user experience considerably.

Asymptotically-Optimal Incentive-Based En-Route Caching Scheme

Content caching at intermediate nodes is a very effective way to optimize the operations of Computer networks, so that future requests can be served without going back to the origin of the content. Several caching techniques have been proposed since the emergence of the concept, including techniques that require major changes to the Internet architecture such as Content Centric Networking. Few of these techniques consider providing caching incentives for the nodes or quality of service guarantees for content owners. In this work, we present a low complexity, distributed, and online algorithm for making caching decisions based on content popularity, while taking into account the aforementioned issues. Our algorithm performs en-route caching. Therefore, it can be integrated with the current TCP/IP model. In order to measure the performance of any online caching algorithm, we define the competitive ratio as the ratio of the performance of the online algorithm in terms of traffic savings to the performance of the optimal offline algorithm that has a complete knowledge of the future. We show that under our settings, no online algorithm can achieve a better competitive ratio than O(log n), where n is the number of nodes in the network. Furthermore, we show that under realistic scenarios, our algorithm has an asymptotically optimal competitive ratio in terms of the number of nodes in the network.

Joint Caching and Routing for Greening Computer Networks with Renewable Energy Sources

The Internet is emerging as a major contributor to the global carbon emission as it consumes in the US alone more energy than that consumed by all of the automotive industry. Content distribution as video on demand represents the majority of the Internet traffic. Therefore, in order to reduce the carbon footprint of the Internet, greener methods for content delivery have to be employed. In this paper, we allow the intermediate nodes to be powered by renewable energy sources, i.e., solar or wind. We also assume that these nodes have a storage capability and can store some of the packets to serve future requests without going back to the source of the contents. Under this model, we formulate the problem of minimizing the brown energy usage, while satisfying the users requests. The problem is formulated as a mixed Integer Linear program. We use a relaxation technique and heuristics to find an efficient solution within 10% of the optimal one in a fast way. We also show that when we code the data, the problem can be formulated using a linear program, which can be computed very fast. Our simulation results show that our optimization framework saves about 40%-90% of the energy consumed by the traditional non-energy aware shortest path routing method.

Joint data placement and flow control for cost-efficient data center networks

We study the problem of cost-efficient operation of data center networks used to deliver heterogenous online services. We split their aggregate cost into server-load-related and link-load-related segments. Thus, we formulate the problem of interest as that of joint data placement and flow control and use mixed integer-linear programming to compute the optimal solution. The high complexity of the latter motivated us to design two additional sets of strategies, based on data coding and heuristics, respectively. In our simulation experiments, carried out based on actual data center information, network topology and link cost, and electricity prices, we examine the advantages of data coding, in particular in the context of multicast, and the impact of different factors such as the network topology and service popularity, on the total cost incured by all strategies we consider. We show that network coding with multicast provides cost savings on the order of 30-80%, depending on the specific context under consideration, relative to the other optimization strategies and heuristic methods that we examine.

Towards efficient operation of internet data center networks: Joint data placement and flow control for cost optimization

Abstract The problem of cost-efficient operation of data center networks used to deliver file sharing services is studied. The aggregate costs are split into server-load-related and link-load-related shares. Thus, the problem of interest is formulated as one of joint data placement and flow control, and mixed integer-linear programming is used to compute the optimal solution. The high complexity of the latter motivated us to design two additional sets of strategies, based on data coding and heuristics, respectively. With coding, a distributed algorithm for the problem is developed. In the simulation experiments, carried out based on actual data center information, network topology and link cost, as well as electricity prices, the advantages of data coding, in particular in the context of multicast, and the impact of different factors such as the network topology and service popularity, on the total cost incurred by all considered strategies, are examined. Network coding with multicast is shown to provide cost savings in the order of 30–80%, depending on the specific context under consideration, relative to the other optimization strategies and heuristic methods examined in this work.

Distributed Online En-Route Caching

Content caching at intermediate nodes is an effective way to optimize the operations of Computer networks, so that future requests can be served without going back to the origin of the content. Several caching techniques have been proposed in literature, including techniques that require major changes to the Internet architecture. In this work, we present a low complexity, distributed, and online caching algorithm based on content popularity. Our algorithm performs en-route caching using a simple cost-reward comparison. Therefore, it can be integrated with the current TCP/IP model. We use the concept of competitive ratio to measure the performance of any online caching algorithm, in terms of traffic savings, with respect to the performance of the optimal offline algorithm that has a complete knowledge of the future. We show that under our settings, no online algorithm can achieve a better competitive ratio than Ω(logn), where n is the number of nodes in the network. Furthermore, we show that under realistic scenarios, our algorithm has an asymptotically optimal competitive ratio in terms of the number of nodes in the network. We also study several extensions to the basic algorithm and show their effectiveness through extensive simulations.