In todays turbulent market conditions, the ability to generate accurate and timely risk measures has become critical to operating successfully, and necessary for survival. Value-at-risk (VaR) is a market standard risk measure used by senior management and regulators to quantify the risk level of a firm's holdings. However, the time-critical nature and dynamic computational workloads of VaR applications, make it essential for computing infrastructures to handle bursts in computing and storage resources needs. This requires on-demand scalability, dynamic provisioning, and the integration of distributed resources. While emerging utility computing services and clouds have the potential for cost-effectively supporting such spikes in resource requirements, integrating clouds with computing platforms and data centers, as well as developing and managing applications to utilize the platform remains a challenge. In this paper, we focus on the dynamic resource requirements of online risk analytics applications and how they can be addressed by cloud environments. Specifically, we demonstrate how the CometCloud autonomic computing engine can support online multi-resolution VaR analytics using and integration of private and Internet cloud resources.

This paper explores the effectiveness of using the CBE platform for Value-at-Risk (VaR) calculations. Specifically, it focuses on the design, optimization and evaluation of pricing European and American stock options across Monte-Carlo VaR scenarios. This analysis is performed on two distinct platforms with CBE processors, i.e., IBM Q22 blade server and the Playstation3 gaming console.

In this paper we present a TCP friendly explicit rate congestion control mechanism for MPLS networks (TERM). The components of the TERM system include a method for using resource reservation protocol (RSVP) to signal explicit rate information, a weight proportional max-min (WPMM) algorithm that calculates weighted fair rates in O(l) time, and a priority based packet marking and queue management system. Simulations validate that, working in conjunction, TERM components increase throughput of TCP and LDP traffic while enforcing weighted fair rates. The unique TERM approach accomplishes this with minimal network and computational overhead.

This paper presents a scalable and flexible system for explicit rate congestion control in multi-protocol label switched networks. Resource reservation protocol is used to signal explicit rate congestion control information for best effort label switched paths. We present a weight proportional max-min fair rate algorithm that works with the signaling mechanism to calculate differentiated fair rates in O(1) time. We propose a method for marking traffic that is above the explicit rate as less than best effort allowing bursty traffic to utilize excess capacity while ensuring all label switched paths get a fair allocation of bandwidth. Simulations are presented that show the effectiveness of the proposed system.