A large number of distributed applications requires continuous and timely processing of
information as it flows from the periphery to the center of the system. Examples include …

In this paper, we present Spade-the System S declarative stream processing engine. System
S is a large-scale, distributed data stream processing middleware under development at IBM …

We describe an approach to elastically scale the performance of a data analytics operator
that is part of a streaming application. Our techniques focus on dynamically adjusting the …

This paper describes the SODA scheduler for System S, a highly scalable distributed stream
processing system. Unlike traditional batch applications, streaming applications are open …

The era of big data has led to the emergence of new real-time distributed stream processing
engines like Apache Storm. We present Stela (STream processing ELAsticity), a stream …

In this paper, we present a stream-based mining algorithm for online anomaly prediction.
Many real-world applications such as data stream analysis requires continuous cluster …

The ever-increasing number of intrusions in public and commercial networks has created
the need for high-speed archival solutions that continuously store streaming network data to …

In this paper, we describe an optimization scheme for fusing compile-time operators into
reasonably-sized run-time software units called processing elements (PEs). Such PEs are …

Large-scale hosting infrastructures require automatic system anomaly management to
achieve continuous system operation. In this paper, we present a novel adaptive runtime …

To reduce cloud system resource cost, application consolidation is a must. In this paper, we
present a novel pattern driven application consolidation (PAC) system to achieve efficient …